Prediction of Dissolved Gas Volume Fraction in Transformer Oil Based on Mixture Decomposition and IDBO-TCN

FU Wenlong; ZHU Xinfeng; WU Chuanfeng; ZHU Fubao; SHAO Mengxin; ZHAO Yibo

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Prediction of Dissolved Gas Volume Fraction in Transformer Oil Based on Mixture Decomposition and IDBO-TCN

更新时间：2025-03-24

- Prediction of Dissolved Gas Volume Fraction in Transformer Oil Based on Mixture Decomposition and IDBO-TCN
- Moren Journal (2025)
- 作者机构：
  
  1.三峡大学梯级水电站运行与控制湖北省重点实验室,湖北省宜昌市443000
  2.三峡大学电气与新能源学院,湖北省宜昌市443000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51741907);Project Supported by State Grid Jilin Province Electric Power Company(2023JBGS-08)
- DOI：
  CLC：
- Received：25 July 2024，
  
  Revised：13 March 2025，
  
  Accepted：13 March 2025，
- 稿件说明：
移动端阅览
FU Wenlong, ZHU Xinfeng, WU Chuanfeng, et al. Prediction of Dissolved Gas Volume Fraction in Transformer Oil Based on Mixture Decomposition and IDBO-TCN[J/OL]. Moren journal, 2025.
DOI：

FU Wenlong, ZHU Xinfeng, WU Chuanfeng, et al. Prediction of Dissolved Gas Volume Fraction in Transformer Oil Based on Mixture Decomposition and IDBO-TCN[J/OL]. Moren journal, 2025. DOI：

摘要

准确预测变压器油中溶解气体的变化规律和趋势是保证变压器安全可靠运行的关键。为此提出一种基于混合分解和IDBO-TCN的油中溶解气体含量预测方法。首先，利用自适应噪声完全集合经验模态分解对给定气体含量序列进行分解；然后，采用经验小波变换对分解出的最高频分量进行二次分解；进一步对所有分量建立时序卷积网络预测模型；同时提出改进蜣螂优化算法对TCN的初始学习率和卷积核大小进行优化；最后，将各分量的预测值累加，得到最终预测结果。通过油中溶解气体CH

、C

、H

和总烃的预测实例证明了所提方法的优越性。

Abstract

Accurate prediction of the change rule and trend of dissolved gas in transformer oil is crucial in ensuring the safe and reliable operation of transformer. For this purpose

a prediction method based on mixture decomposition and IDBO-TCN is proposed to predict dissolved gases content in transformer oil. Firstly

the given gas content sequence is decomposed by using complete ensemble empirical mode decomposition with adaptive noise. Subsequently

empirical wavelet transform is applied to perform a second decomposition of the highest frequency component. Then

temporal convolutional network is established for all components. Simultaneously

the improved dung beetle optimization is proposed to optimize the initial learning rate and convolution kernel size of TCN. Finally

predicted values of each component are accumulated to obtain the final prediction result. The advantages of the proposed method are demonstrated by predicting the dissolved gases CH

and total hydrocarbons in oil.

关键词

Keywords

references

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